In each model, for the feedforward networks \({\phi }\) , \({\eta }\)  and \({\xi }\)  referred to in Equations (REF ), () and (), we use 2 layers with 600 units, ReLU activations and a skip connection. We train each model using stochastic gradient descent with a learning rate of 0.001 and Nesterov momentum of 0.9 [1]}. We train for a maximum of 100,000 iterations, using early stopping on the validation set.
